Quantum Computing technology could eventually allow computers to perform at the levels of human analysts in the field of predictive analytics. The advancement of quantum computer technology could revolutionize the intelligence field of predictive analytics. Artificial intelligence is a subject of controversy, and some experts do not place a high degree of expectation that computer capabilities will advance enough to exercise unsupervised analysis near the abilities of human beings.
Computer technology has long been recognized as being superior to the human brain’s ability to carry out certain tasks. Human analysts have been utilizing computer programs for several decades. Computers have been used to calculate large equations and record information in a permanent manner; in that way, human analysts have been able to successfully incorporate computer technology in the overall intelligence process.
Computer technology seems to be incapable of carrying out certain intelligence-related tasks; computers have typically not been able to solve certain types of problems or learn in a way that is as sophisticated and complex as human beings are capable of learning.
Computer programs are not able to perform innovative cognitive functions in a way that equals the capabilities of human analysts; for example, computers are feeble in terms of being able to offer original solutions to problems, and machine learning is largely limited to programs merely matching data trends.
Human beings are much better than computer technology when the analysis includes associating data dynamically. Human beings are superior to computers in terms of being able to apply old data to new situations in order to solve problems; according to Chomsky and Danaylov (2013), Chomsky’s research has led him to believe that scientists do not truly understand how an animal’s neurons manage to differentiate between beneficial and deadly objects in the environment.
The scientific community is currently encountering significant difficulties in the effort to invent artificial intelligence; AI technology is typically designed in a manner that mimics the intelligence capabilities that are exercised by biological creatures, but such scientific efforts have failed to invent truly intelligent computer technology. The ability for scientists to design genuinely intelligent computer programs would entail giving such technology the ability to make rational choices, and scientists have no idea how to write such programs (Chomsky and Danaylov 2013).
The complexity of the human brain appears to be far beyond the true comprehension of computer scientists. Experts in the field of artificial intelligence may be correct when deciding to examine how the brain functions in order to design better computer programs, but that particular technique is very limiting due to the fact that the scientific community does not possess a clear understanding of how the brain allows for cognitive thought. Computers may only be capable of performing in the capacity of advanced automatons if scientists only attempt to create computers that copy the brain’s capabilities.
Developers of quantum computer technology do not understand how to design computers that are intelligent in the exact same ways as human beings are intelligent; however, the development of quantum computers may allow analytical technology to carry out sophisticated problem solving and machine learning tasks.
The strategy of patterning computer behavior after human behavior may not successfully bring about artificially intelligent analytical machines. The ability to write computer codes specifically intended to bring about rational thought in computer technology seems to be a very difficult objective at the present time.
Analytical thought can only be introduced in computer technology if the human designers understand how rational thought is produced, but experts can argue that simply inventing programs to resemble human analytical capabilities will not allow for the advancement of true AI; according to Chomsky and Danaylov (2013), Chomsky does not believe that the Turing Test is a genuine method of determining if a program exhibits artificial intelligence; the Turing Test is a game in which computer programs are made to imitate human intelligence, but an imitation of intelligence is not necessarily the same as being genuinely intelligent.
Scientific efforts to reverse engineer the human brain are not likely to bring about artificial intelligence because such brute-force methods rarely lead to a true understanding of how complex systems work. Machines are only as intelligent as computer programs that are written by human beings, and even the most advanced computer analogues seem incapable of significantly understanding or offering unique insights (Chomsky and Danaylov 2013).
Serious doubts may be cast upon the possibilities of quantum computing technology someday offering the reality of artificially intelligent analysts. Critics may argue that scientists simply do not know how to write intelligence offering analogues or design neuron inspired computers that could match the intelligence capacities of the human brain.
The emerging field of quantum computers may revolutionize the field of artificial intelligence and dispel doubts in the possibilities of AI. No one truly understands the potential of quantum computing at the current time. Extensive research is still nonetheless needed in order to determine the benefits that quantum computer technology may someday bring to AI and predictive analytics.
The difficulties of writing artificially intelligent computer programs are daunting; however, quantum computing may eventually allow for new kinds of intelligence to emerge and greatly benefit the field of predictive analytics. Experts are currently working towards the advancement of cyber-technology that is used in predictive analytics.
Quantum computing is seen by some developers of analytical technology as a revolutionary method that could expand artificial intelligence capacities. Engineers at IBM are already speculating how Watson Analytics could be improved by the incorporation of quantum computing; according to Cuthbertson (2015), developers at IBM believe that Watson Analytics is highly compatible with quantum computing technology, and Watson would extremely benefit from that emerging computer technology.
A quantum version of Watson Analytics is expected by IBM to someday be developed, and the experts that are currently working towards that objective believe that a quantum Watson would be vastly more intelligent than the current classical computer version that is on the market today (Cuthbertson 2015). Experts working in the field of predictive analytics are only able to speculate how analytical cyber-technology would be advanced by quantum computing; nonetheless, much of that speculation is very positive.
Research that concerns quantum computing’s potential role in the field of predictive analytics could help motivate talented professionals and raise needed funds for further developmental projects. While quantum computers may someday allow for sophisticated computer-based predictive analysis to take place, research is needed in order to help determine if such advancements are someday possible.
Review of the Literature:
The research concerns the unanswered question of in what ways quantum computers will benefit unsupervised computer-based predictive analytics, and part of that larger question is examining the feasibility to actually design fully functioning quantum computers that exhibit a diversity of operational capabilities.
Quantum computing is a newly emerging field of science. Uncertainties currently exist regarding the possibilities of creating quantum computers on a large scale. Challenges have been discovered by computer scientists that are studying and working on quantum computer technology. The question still remains if the emerging technology of quantum computers is indeed possible to create on a fully functioning level.
This debate stems from the concerns over the instability of quantum particles. The issue is addressed by the source by Fedichkin, Yanchenko and Valiev (2000), the issue of quantum de-coherence rates have been studied, and it would seem that qubits are indeed “coherent enough to perform error correction procedures” (Fedichkin, Yanchenko and Valiev 2000).
Therefore, given that bright minds have been successfully working on quantum computer technology for years, and have made incremental gains in their endeavours, quantum computers will most likely be created in the near future. The resources mentioned above only reflect a very small fraction of the many works of research and other publications which readily exist that may be of benefit to the proposed research.The wide-spread application of quantum computer networks is a daunting challenge.
Quantum computers have a tendency to crash. The unstable nature of sub-atomic particles can lead to errors often cropping up during quantum computer functions. Computer scientists are working on ways to mitigate the problem of errors taking place when quantum computers are running; according to Hsu (2015), a primary challenge in the creation of a working quantum computer is the fact that the binary representing quantum particles known as qubits are notorious for inherent instability. Scientists are making progress in the endeavour to develop ways in which the quantum computers themselves will exhibit qubit error corrections.
Google has funded a development team from University California, Santa Barbara in order to further advance quantum computer error correction; the researchers had already had success in terms of creating superconductor technology designed for quantum computing. The new project that Google is funding will allow for error correction of qubits to take place in a manner that would allow for quantum computers to function without suffering from performance failures while running (Hsu 2015). Therefore, despite the fact that quantum computers are often prone to errors that end up jamming the performance of the devices, scientists have been successful thus far in early attempts at reducing such error problems.
Quantum computer scientists could make more progress in terms of reducing quantum machine errors if more funding and resources were invested in quantum computer science. Quantum computers operate under the laws of quantum physics, and such laws are often times very unpredictable.
Quantum computers use the entanglement process of sub-atomic particles in order to function. Quantum entanglement does not often stay in place when longer distances of communication are involved between quantum computer technologies; according to Zhong, Hedges, Ahlefeldt, Bartholomew, Beavan, Wittig, Longdell, and Sellars (2015), communication of around one hundred miles in distance via a theoretical quantum computer network is made difficult due to the fact that quantum entanglement tends to break down over such large distances. Designers of such a network could theoretically utilize a repeater protocol that preserves quantum information that may have been lost during the long-distance transitions.
The proposed quantum-based repeater protocols would depend upon the concerned quantum entanglements having reasonably stable lifespans (Zhong, Hedges, Ahlefeldt, Bartholomew, Beavan, Wittig, Longdell, and Sellars 2015). Quantum computer networks may have the tendency to crash because of the inherent nature of quantum particles to change states. Classical computer technology may be more trustworthy in terms of running without incidents of crashes.
Computer scientists are attempting to discover ways that would solve the problems that come along with developing quantum computers that can run without losing information every time the quantum particles inside of the chips change quantum states. Scientists have already devised a variety of qubit models, and the unpredictable nature of quantum particles has been a serious consideration during the design phases of such qubits.
The quantum bits are stored in computer chips; the fundamental concept of a computer chip has survived from the classical computer age to the quantum computer age. The manner in which quantum bits are encased inside quantum computer chips seem to have significant repercussions in terms of the stability of the quantum particles that are inside such chips; according to Zhong, Hedges, Ahlefeldt, Bartholomew, Beavan, Wittig, Longdell, and Sellars (2015), researchers had been able to reach a maximum coherence time that lasts three hours by means of using a silicon-28 that works with a phosphorus donor.
Zhong’s team broke the coherence record and reached the six-hour mark by using a technique that utilizes europium-doped being placed inside yttrium orthosilicate during the creation and manipulation of the qubits. Quantum memory can be much more stable by using the europium technique pioneered by Zhong’s research team because of the optical addressability offered by the matrix utilized during the quantum manipulations.
Long-range quantum computer transmissions tend to be best approached through the use of photons due to the fact that such optic-based transmission is not difficult to create and send; but, ranges of communication that range from around a mile of more present challenges, such as the light being absorbed, spread out, or absorbed during the lengthy path between the transmitter and receiver in a network (Zhong, Hedges, Ahlefeldt, Bartholomew, Beavan, Wittig, Longdell, and Sellars 2015).
The more advanced studies that have been taking place concerning the medium by which quantum computers store and manipulate sub-atomic particles in entangled states have so far shown promise in regard to keeping the qubits in entangled states. Qubits existing longer in entangled states will allow for long-distance communication via quantum computers; hence, cloud networks and Big Data could be theoretically stored and communicated through quantum computer technology in the future.
The intelligence field of predictive analytics depends heavily upon the analysis of Big Data. Big Data analysis is often a crucial element in the intelligence process. Computer programs are being utilized by analysts in order to make sense of Big Data; the size of Big Data streams can be overwhelming to human analysts.
Ying (2014), explains that the current era of Big Data presents challenges in the business world due to the chaotic assortment of information that business analysts must make sense out of in order to produce intelligence reports to consumers (Ying 2014). Therefore, with the rise of Big Data that seems to exist and increase during the current information age, the need for computer technology assisting human analysts is understandable. Because computers are naturally effective at processing the massive amounts of information present in Big Data, the trend in the intelligence field is to increasingly utilize computer technology for such tasks.
The job of predictive analysts would be made easier if computers could offer predictions. However, differences exist in terms of how effective computer technology has performed in the varied aspects of the intelligence production cycle; computers seem to be particularly limited in terms of performing predictive analytics when compared to the performance abilities of human analysts.
Human beings are still much better than computers at predicting future events based upon the analysis of data; however, the analysis of Big Data will almost certainly entail humans utilizing computer technology in order to make sense of the ever-increasing volumes of information that is being created and stored.
The mass of information that exists inside Big Data may become increasingly overwhelming for human analysts to handle without some form of computer technology being utilized; according to The Moore’s Law of Big Data (2013), the expansion of digital media that is part of Big Data is roughly keeping pace with the advancements in processor power predicted by Moore’s Law. Moore’s Law predicts that computer processing power will become twice as efficient within every two years because of the increasing advances in the construction and application of transistors inside a computer’s processors.
The increase of digitalized media that is included in Big Data may be increasing to twice as large roughly every two years, and such Big Data helps analysts discover truths about the world which is valuable to industrial research. The increase in processing power that has come with the fulfilment of Moore’s Law over the years has given Big Data analysts better tools by which to find, record, and make sense of vast amounts of information (The Moore’s Law of Big Data 2013).
The exponentially increasing volumes of Big Data almost guarantees that on many occasions, human analysts will need to rely on computer technology in order to efficiently analyze Big Data, but uncertainty exists in regard to if computer processing power will continue to keep pace with the expansion of Big Data volumes.
A possible future in which the volume of Big Data exceeds the processing power of computers to effectively analyze may someday come into reality. Processing power has thus far stayed true to Moore’s Law. The continued advancement of computer processors may come to a grinding halt due to the realities of nanotechnology being used in computer circuits; according to Albert, Simmons, and Samoilov (2016), Moore’s Law is a phenomenon regarding the units and processing speeds of CPUs and has been a mainstay in the prediction that computer power will level up to twice as powerful roughly every other year.
The continued validity of Moore’s Law is coming into doubt; due to the fact that parts inside CPUs are becoming smaller and smaller, as the incredibly diverse amount of microscopic parts are becoming less likely to be made smaller and still function. Processor parts will eventually become smaller than atoms, and the laws of physics then switch from classical physics to that of quantum physics; hence, processors would have to eventually function in the realm of quantum physics as Moore’s Law continues to progress (Albert, Simmons, and Samoilov 2016).
The realities of the laws of physics may halt the continued and incremental increase in processing speeds that have occurred over the progression of computer technology; if processing speed stagnates, then the volumes of Big Data would theoretically become more difficult for computer technology to handle, and the field of predictive analytics would become less reliable in the future.
Computer engineers may be able to mitigate the problem of Moore’s Law becoming an obsolete prediction if the nanotechnology used in processors could be made to work correctly at the sub-atomic level; according to the quantum computer scientist named Michelle Simmons (2012), the computer industry has spent very large funds in order to figure out how to decrease the size of functioning parts in processors.
The components of modern CPUs are now nanotechnology, with parts in CPUs being many thousandths of times smaller than the thickness of a strand of hair. The incremental progression of miniaturizing computer technology has been a conscious effort; the computer industry has made sure to prove Moore’s Law correct by scaling down the parts used in processors. The steady reduction in the size of transistors inside processors has allowed computers to function with more and more processing power; modern processors that utilize billions of transistors working inside every chip.
When researchers work on the predictable assumption that Moore’s Law will continue to be proven right, as has been the case for decades, then by the year 2020, transistors will be as small as atoms. When transistors would eventually be miniaturized to the state of atomic size and then smaller, the realm of classical physics no longer apply, and the laws of quantum physics take over; due to the fact that sub-atomic particles function as waves as well as particles, the computational mechanics of classical CPUs would no longer function correctly.
Moore’s Law can be used to predict that without the advent of quantum computer technology, processing power can only advance for roughly another half-decade, and then stagnate. Moore’s Law can only continue to be correct after another half-decade if quantum computers can be used to continue the advancement of processing power (Simmons 2012).
Classical computer technology will almost certainly begin to stagnate in terms of processing power by the end of the decade; however, quantum technology may serve as a solution to the eventual problems of creating nanotechnology that works at the sub-atomic level. The continued validity of Moore’s Law is essential if computer technology will be able to assist human analysts tackling Big Data; otherwise, practitioners working in the field of predictive analytics will increasingly encounter more and more difficulty attempting to produce predictive intelligence reports based on Big Data.
The challenge of sub-atomic nanotechnology development in computers could be a blessing in disguise if quantum computing is successfully advanced in the field of computer science; according to Simmons (2012), the plus side to the invention and use of quantum computers being used by society is that quantum-based processors would theoretically be much faster than the classical processors used today.
A classical processor’s transistors can only perform one action at a time. Quantum processors utilize transistors that can perform different actions at the same time (Simmons 2012). Therefore, processing speed is much faster when a computer runs on qubits instead of classical bits. Quantum computers would theoretically allow for much faster processing power if the technology is able to be advanced.
The logic behind why quantum computers would be much more powerful is that many computational steps could be completed in the same time intervals, and the fact that transistors would be able to perform more than one function at the same time, then the size of the chips could also be reduced; hence, allowing for faster processing and less space being needed for computer functions. While modern classical computers run on a very large amount of transistors in order for computers to offer the processing speeds that exist today.
Quantum computers would, in theory, only need to run on a fraction of the transistors as classical computers need to run. The speeds of quantum computers would be adequate; according to Simmons (2012), every time a quantum bit transistor, known as a qubit, is added to a CPU, the processing power is made twice as powerful by just one added qubit, and this fact is expected to allow for exponential speedups in processing power as quantum computing advances in the future.
Quantum computers are expected to become ideal forms of information technology that can be used to crunch Big Data, and thus be used by researchers to model economies and the environment; the vast amounts of unknown outcomes that are inherent in Big Data models would be effectively handled by quantum computers because of the inborn capabilities that qubit powered processors would possess (Simmons 2012).
Quantum computing technology may be able to solve the problems inherent to the continued existence of Moore’s Law. Quantum computers may prove to be much more effective than classical computer technology in a wide swath of applications. The possibilities of enhanced processing power that would be the result of quantum computers being further developed may lead to more advanced forms of artificial intelligence.
Computer technology has long been designed around the concept of binary language. Bits have served as the primary method that computers are able to process information in binary language; binary is a language that uses ones and zeroes in order to transmit and store information.
The inherent superiority of quantum computing technology over that of classical computers lays in the difference between a bit and a qubit; according to Albert, Simmons, and Samoilov (2016), in the processors of classical computers, binary is expressed as either a one or a zero; a one is a small amount of electricity that is stored, and a zero is the absence of electrical storage. Binary is the hallmark method of processor functionality, and the segments of binary language that make up individual statements are called bits; binary language works at the macro-level of classical physics.
Quantum-bits are abbreviated as qubits, and also express the language of ones and zeroes; however, the particles that are manipulated by a quantum computer in order to read and write qubits entail sub-atomic particles. The spins and polarized states of sub-atomic particles are used in the rendering of quantum binary computer language. Quantum binary methods allow for a computer to tackle a question by crunching parts of equations in parallel time; the use of qubits allow for computers to work out questions by performing computational measures all at once instead of a part by part, and the speed of the processor is then much faster than a classical CPU.
The parallel processes of a quantum chip are owed to the fact that a sub-atomic particle’s spin can be positive, negative, and both positive and negative; a sub-atomic particle is not limited in terms of the sequential reality that the macro- realm of classical computers must operate (Albert, Simmons, and Samoilov 2016).
Quantum bits may be used to vastly increase processing speeds because of the laws of quantum physics allowing for faster computation. The application of quantum computing in the field of predictive analytics may allow analysts to make sense of Big Data in a way that classical computer technology cannot; however, because of the fact that quantum computing is still in the very early stages of development, the true potential of quantum computers being used in the practice of predictive analytics is not yet known.
Quantum bits can be designed by computer scientists, but the actual application of qubit processors into other areas of computer technology may prove to be a difficult task. Areas of uncertainty exist in the field of quantum computer development, and such uncertainty would have to be addressed before qubits could be more widely applied to computer-assisted predictive analytics; according to Albert, Simmons, and Samoilov (2016), quantum bits can exist in super-positional states that allow for more processes in the chip to take place at once, and this is why a quantum computer would theoretically crunch data faster than a classical computer.
Quantum bits do not need to operate independently of each other while crunching data in a CPU; instead, the laws of quantum mechanics allow for the qubits to be intertwined in states of entanglement, which is capable of further speeding-up a computer’s processing capabilities. De-coherence is a major problem in terms of quantum computers; when super-positions break down due to interference from the outside world, a quantum computer loses the ability to properly function. Great resources must be invested in order to meet the necessary environments that quantum computers need in order to correctly process information.
The puzzle of how information in a qubit based CPU can be moved onto a hard drive’s ROM or RAM is another hurdle for developers; qubits are easily disturbed by the outside environment, and this means that moving information from the CPU of a quantum computer is a problematic step during such a computer’s functions. Quantum computers are expected to someday be much more efficient than classical computers in terms of searching the World Wide Web for information and making sense of Big Data (Albert, Simmons, and Samoilov 2016).
Qubits could theoretically revolutionize computer technology and allow for much more accurate predictive analytics reports, but while such advances may lay in the future, quantum computer technology has not yet been developed enough to fulfil such expectations at the present time. Quantum computer technology would be very useful for human analysts attempting to produce predictive reports based on the ever-growing volumes of Big Data; however, faster processing speeds that may be available after the development of quantum computing would not necessarily be able to allow computers to exhibit the problem-solving skills needed to produce predictive intelligence reports independent of human analysts.
Human analysts are capable of producing predictive intelligence reports based on the analysis of Big Data. Computer technology has proved to be an effective tool for human analysts, but computer technology is certainly limited in terms of being able to produce predictive analytics reports without a human being involved in the analysis of Big Data; according to FICO’s How Does Predictive Analytics Differ from Data Mining and Business Intelligence? (2006), there is a difference between the related disciplines of data mining and predictive intelligence.
Human beings are needed to supply predictive intelligence, and although there are cyber-technology suites that are offered to customers that are marketed as being able to produce predictive analytic reports to consumers, such reports are merely based on the pattern recognition of commercial trends; computer-generated predictive analytics reports are rather simplistic in nature (How Does Predictive Analytics Differ from Data Mining and Business Intelligence? 2006).
This use of computers in the creation of predictive analytics reports is nonetheless being challenged due to recent computer technology advances. A report from IBM titled What is Watson Analytics? (2015), claims that the cloud-based service known as Watson Analytics offers its customers a low effort and computer-generated means of obtaining useful predictive analytics reports which enable users to make wise decisions for the futures of their endeavours (What is Watson Analytics? 2015).
The nature of Watson Analytics is also a subject that has been covered by previous research. Watson may be merely able to retrieve data in order to answer questions faster than human contestants in a trivia game show. Rapid data retrieval and information linkage is not necessarily the same function as AI-based problem-solving skills. The questions over whether or not Watson analytics is actually a form of artificial intelligence should be addressed.
Artificial intelligence is understood by experts to entail being able to think in a way that is similar to a human being. Computer scientists that have been active in developing Watson made efforts to ensure that Watson’s processing of information was more sophisticated than the simple retrieval of information that earlier predictive analytical technology relies upon in order to serve consumers; according to Lohr (2011), the US Department of Defence-funded the creation of Watson Analytics.
Watson Analytics is inspired by the neuronal functions found in the human brain, and this specific neuronal design is believed to be a primary reason why Watson is capable of analytical skills (Lohr 2011). The evidence clearly indicates that IBM’s Watson displays functions which are beyond mere data retrieval based on information input. The possible AI functionality of Watson may be due to a computation design loosely patterned after the human brain; according to Siegelmann and Sontag (1991), the concept of creating computer technology that is designed as a reverse-engineered brain that uses binary logic gates which function in a manner similar to neurons has been researched for decades (Siegelmann and Sontag 1991).
Watson may indeed possess true problem-solving capabilities which are needed to produce unsupervised predictive analytics reports. Watson does possess dynamic predictive functionality; however, whether or not Watson is capable of AI capacities is still a matter of debate. The problem-solving capabilities of Watson’s system are almost certainly far less advanced than a human being’s problem-solving abilities.
The simple fact that IBM’s Watson has been created with a computation design inspired by the human brain does not necessarily mean that the actual functionality of Watson fundamentally resembles the cognitive functions of a human brain. Watson’s problem solving capacities is still a matter of debate among experts in the field of AI; according to Chomsky and Danaylov (2013), Chomsky argues that the human brain works intrinsically unlike any of the current attempts at AI.
Computers will most likely not be able to solve problems on their own during the current age of computer development. Chomsky grudgingly admits that some computer technology may be capable of exhibiting some problem-solving skills, but he stresses that computers are limited and primitive in respect to true problem-solving abilities of a human brain (Chomsky and Danaylov 2013). The debate over Watson’s true capacities and usefulness are ongoing, and certainly, IBM’s Watson has been criticized as being little more than a useful prop used by human analysts.
The ability to practice problem-solving skills is not as advanced in computer technology such a Watson Analytics as is the human brain’s abilities to do so. Critics of IBM’s Watson may be unfair in terms of belittling that technology’s usefulness to analysts. Advocates of Watson’s possible AI capacities may have overemphasized the product’s AI characteristics as a result of market realities; customers of predictive technology that have been told that a product is capable of exhibiting AI level functionality may be more inclined to buy that product.
Computers capable of exercising artificial intelligence may be invented in order to exercise problem-solving abilities, the application of Bayesian networks into such computer technology may be one of the essential elements needed for that cyber-advancement. The source by Charniak (1991), explains that researchers and theorists in the fields of probability and uncertainty have increasingly been embracing the methodology known as Bayesian networks as a means to eventually design computer technology to function as AI, and this would theoretically enable such computer technology to predict outcomes based on incomplete information (Charniak 1991).
Bayesian networks methods of analysis can be a complex discipline, which is known to use what is known by theorists as click trees, which may be very useful in the emerging field of AI. The source by Mengshoel (2010), explains that Bayesian networks are enabled by the use of methods called clique tree clustering as well as propagation; which entails propagation that takes place by way of clique tree propagation that is composed out of a Bayesian network (Mengshoel 2010). The methods by which Bayesian networks reach conclusions have been established for some time.
Probability theorists have argued that such methods could be useful in the advancement of computer technology that may someday be able to produce predictive analytics reports on at least the same quality level as human analysts. Bayesian networks may someday be incorporated to help quantum computer technology make AI level analysis. The use of Bayesian networks being more heavily integrated into computer technology such as Watson alone may not allow computers to advance to the AI levels needed to independently practice predictive analytics.
The processing of Big Data may be too much of a task to allow Bayesian networks to be fully and properly worked out by computers. When a very large amount of information needs to be worked out in a Bayesian formula, there simply may not be enough time for analysts to wait for computer outputs.
The development of quantum computer technology may give a solution to the problem of inordinate time that is sometimes spent analyzing Big Data; the source by Cuthbertson (2015), explains that the emerging technology of quantum computing will be able to someday dramatically increase computer speeds and advance machine learning (Cuthbertson 2015).
Therefore, when considering the value of Big Data being analyzed by way of Bayesian networks, the advantages of using quantum computers for such tasks is obvious; the faster the processing time a quantum computer is able to perform, the more Big Data can be analyzed through methods such as Bayesian networks; artificial intelligence may be approached through such combined methods, and consumers could greatly benefit from improvements in predictive analytics reports.
Machine learning is made possible by algorithms which allow for computers to adapt or learn. Computers are often not dependent on constant human supervision and re-programming; according to Machine Learning (No date), machine learning is when a computer is capable of discovering insights about the world based on analyzing data that is stored inside the computer’s memory. Computers which lack machine learning capabilities cannot evolve to changing circumstances and realities that pertain to the data which the computer receives and analyzes; such computers have no ability to amend conclusions and will depend on human beings to fulfil that role.
Machine learning is an established concept that has gained popularity in recent years because of the developments of more advanced algorithms that display the ability to process and analyze Big Data much faster and more efficiently than older machine learning algorithms. Recent forms of technology that is being marketed today; self-driving cars that are designed by Google, algorithms which analyze tracking cookies in order to more efficiently market advertisements to potential customers, and algorithms that are designed to analyze discussions that take place over social media, or detect fraud that is taking place are all examples of machine learning.
The increased speed and processing power associated with modern machine learning algorithms are enhanced by the application of linguistic rule creation, the computer’s capacity to rapidly process streams of data in order to create working data models, and Bayesian analysis (Machine Learning No date). Therefore, machine learning is not achieved by the simple application of one form of technology or scientific discipline; a synergy of different scientific techniques must be applied in order for machine learning to be achieved. The many ways in which machine learning could benefit society is impressive, but further advances appear to be needed in order for such applications of machine learning to become wide-spread.
The application of machine learning in the field of predictive analytics seems very plausible. Big Data analysis is a fundamental component of predictive analytics. Computer algorithms that are written for the purposes of machine learning may be applicable towards the tasks that entail crunching Big Data; according to Machine Learning (No date), the growing amounts of Big Data have increasingly allowed machine learning algorithms to analyze more data, and hence give more informed outputs to consumers.
The writing of advanced machine learning algorithms that are designed to work in conjunction with Bayesian analysis methods and linguistic rules have brought about computer-based predictive analytics that gives outputs to customers; while human beings are needed in the design and development of such machines, humans are not needed during the computer’s analysis and prediction output functions (Machine Learning No date).
The understanding among experts in the field of machine learning is that the faster new technology is able to achieve information processing, the more advanced and dependable a machine running a learning algorithm can be for consumers. The advantage of using new quantum computer technology in the near future is the increased processing speed which could be offered during machine learning.
Machine learning algorithms seem to allow computers to learn from new data in a manner that is not dissimilar to how biological brains learn from the external environment; according to Domingos (No date), computer scientists have created special algorithms which amend the behaviour of computer programs based on heuristic-based modification, which is essentially known as machine learning; computer algorithms can be written that are designed to expect that patterns and trends will play out in similar manners as have past patterns and trends; and hence, these machine learning algorithms are able to give predictive advice to human consumers.
Machine learning algorithms have been widely used in the marketing of commercial products, the designing of medicine, and cyber-security (Domingos No date). While machine learning algorithms may benefit a host of technology and intelligence-related fields, such algorithms do not appear to possess the ability to come up with original conclusions; everything that a computer can learn while running on a learning algorithm is strictly related to past trend patterns.
Human analysts would seem to possess a comparative advantage in terms of innovative thought over computers that run on learning algorithms. The fact that a machine-learning algorithm is able to practice a reasonable amount of autonomy may be encouraging for advocates of the possibilities of AI. The use of machine learning algorithms may be of benefit to human analysts that can rely upon computer technology to handle much of the bulk of Big Data; according to Domingos (No date), machine learning algorithms are able to change and adapt in accordance to changing data patterns which are analyzed by those algorithms, and human beings are not needed in order for machine learning algorithms to evolve in response to new data patterns.
Algorithms for machine learning are heavily utilized in the fields of data mining and predictive analytics. Algorithms are widely designed to operate as classifiers; a classifier essentially takes in data for analysis, and upon the completion of the analysis of the data flow, gives an output that is used by the human consumption in order to make informed decisions; the classification process is oftentimes accomplished by means of Boolean vector equation templates that can be modified to analyze different data streams which are fed into a computer for subsequent analysis by the machine learning algorithm.
Computer algorithms can be designed to heuristically make generalizations about new data streams, and such generalized expectations use pattern recognition that the learning algorithms find in older data streams. The learning algorithms are able to learn from past data stream patterns and trends in order to make predictions about trends and patterns found to be associated with new data streams (Domingos No date). Therefore, developers of machine learning algorithms would be able to make significant advances in the field of predictive analytics if the speed and flexibility of computer processing could be enhanced, since quantum computers may offer such advantages, there appears the very real possibility that machine learning would reach much more dependable and complex levels of performance once quantum computer technology is widely introduced in the field of machine learning.
Machine learning algorithms may at times have trouble correctly analyzing and adapting to vast amounts of diversified Big Data. Quantum computers are expected to thrive and reach better performance levels when running and analyzing such vast data streams through a learning algorithm; according to Lloyd, Mohseni, and Rebentrost (2013), the classification of data stream vectors by use of machine learning algorithms tend to rely upon operations of equations that concern the numerical and special dimensions of vectors.
Quantum computational methods are particularly effective at manipulating vectors that are composed of complex dimensional characteristics, researchers believe that future machine learning algorithms which would incorporate quantum computing techniques would exhibit higher learning capabilities than the current non-quantum machine learning technology used today. Machine learning algorithms typically perform best when such algorithms are given Big Data in the form of tensor products that are composed of vectors and vectors of data that are fed as a data-stream (Lloyd, Mohseni, and Rebentrost 2013).
Quantum computing and learning algorithms are potentially very compatible with each other; both forms of computer technology are beneficial in terms of crunching large amounts of information. The future of predictive analytics may someday be altered for the better by the coupling of learning algorithms and quantum computation; however, difficulties may arise when computer engineers attempt to figure out methods of designing qubit processors to actually be able to run complex learning algorithms, which may or may not be feasibly possible.
The advantages to eventually switching over to quantum computers in order to crunch data is somewhat obvious, and the field of predictive analytics would most likely benefit from the incorporation of quantum computer technology; according to Lloyd, Mohseni, and Rebentrost (2013), quantum computational methods are inherently better at analyzing Big Data than traditional computational methods; researchers believe that quantum-based machine learning algorithmic methods will be more effective at learning than current traditional machine learning algorithms that do not incorporate quantum computing.
Quantum computers would be able to store data in the q-RAM so as to be read in the form of sub-atomic particles, which is then run through a quantum-based learning algorithm; at which point, the quantum computer’s machine learning algorithm would be notably faster than what would normally ever be performed by a traditional computer’s machine learning algorithm processing time (Lloyd, Mohseni, and Rebentrost 2013). Therefore, when considering how quantum computers would work in support of learning algorithms, the outlook appears very positive in terms of the beneficial role that quantum computers would play in the field of predictive analytics, and this is because of the strengths that would come along with using quantum computers to analyze very large amounts of information.
While classical computer technology is capable of crunching Big Data, quantum computers would probably be better for the task; the difficulties appear to lay in actually developing the needed quantum technology that could be used to analyze Big Data. The architecture of classical computer technology, such as RAM and algorithms are written in coding language appear to be the primary designs in which quantum computer scientists have been planning to devise quantum machine learning; however, the architecture of the human brain may be an optimal model to examine for quantum computer scientists.
Researchers have speculated that the human brain is in fact a biologically based quantum computer and that designing quantum computers in a way that resembles human brain structure could exponentially improve artificial intelligence performance; according to Rigatos and Tzafestas (2006), if human brains use some form of biologically based quantum computational functions, such as brain synapses playing the role of fuzzy variables during brain processes, then there is the very real possibility that the human brain runs quantum-based learning algorithms wherein associative memories may exist in super-positional states.
Human brains may utilize quantum super-positional states to store and associate memories together in meaningful patterns by way of forming vectors of data that are represented in the form of sub-atomic particles which form relational attractor patterns by means of unitary rotations (Rigatos and Tzafestas 2006).
The fact that the human brain is in overall terms, much more effective at such tasks as learning and problem solving may be a result of the dynamic methods by which the brain stores memory; instead of using a static method such as computer RAM, the brain may in indeed be utilizing quantum particles to store and learn associations between memories.
Theories which speculate that the brain utilizes quantum particles to store and make sense of memories is new, and many questions still remain to be answered in regard to the validity of such claims, as well as the methods by which the brain may be accomplishing such a task; however, if such a cognitive phenomenon is being performed by the human brain, then the superiority of human intelligence over computer technology seems easier to understand; according to Rigatos and Tzafestas (2006), traditional computers which are non-neuronal inspired are inferior to the human brain in terms of such tasks as storing and associating memories.
Scientists speculate that if new computer technology were created which essentially take the form of synthetic neuronal design and functionality, then such new computers would almost certainly be much better at associating and constructing meaningful patterns of memorized data (Rigatos and Tzafestas 2006). Quantum computer engineers may need only observe and understand the quantum level functions of the brain in order to design artificial intelligence.
The theory of brain science which has proposed the existence of the brain’s quantum level cognitive functions still needs to be proven, but the endeavour to more fully understand the brain’s quantum behaviours could be a positive learning experience for brain scientists and computer scientists alike. Quantum computers would benefit in terms of functionality by being modelled after quantum brain functions, and in order for quantum computer scientists to achieve such an accomplishment.
The neuronal architecture of the brain’s biological networks would most likely have to be synthetically replicated by quantum computer engineers; according to Rigatos and Tzafestas (2006), neural inspired quantum computers would be better at learning as a result of mimicking the enhanced memory associative capabilities that are believed to be fundamentally exhibited by the human brain (Rigatos and Tzafestas 2006).
The debate over whether or not computers could ever be designed to function at the level of AI may end up being settled if computers are someday designed to function in a capacity that resembles the human brain, as well as any quantum cognitive functions which take place in the human brain; therefore, if the speculations are indeed true that human brains utilize quantum particles in order to process information about the external environment, then the next obvious step in terms of the advancement of machine learning would be to further develop quantum computers that are inspired by the human brain.
The human brain is increasingly becoming a source of guidance for computer scientists; developers hope to create more advanced computer technology by learning from brain functions during cognitive processes; according to Angelica and Schmidhuber (2012), the Swiss Artificial Intelligence Lab (SAIL) has designed computer technology that is designed to exhibit machine learning capabilities. The SAIL has led pioneering efforts to design computer technology that is inspired by the neuronal functions of biological brains. SAIL has tested the neuronal inspired computers in imagery pattern recognition tests as a way to ascertain if machine learning is being advanced by way of designing computer technology in a manner that is similar in some respects to human brains (Angelica and Schmidhuber 2012).
The incorporation of neuronal cognition designs into computer technology has proved to be an effective pioneering effort on the part of computer developers. The computer technology that has been invented by the Swiss Artificial Intelligence Lab has been classical computing instead of quantum computing; however, a synergy between neuronal inspired computers and quantum computers may allow for computer science to advance to new heights of performance capabilities. Machine learning and problem-solving capabilities can be compared to that of the human capacity to learn.
The Swiss Artificial Intelligence Lab’s computer technology has been tested against human beings; according to Markoff (2012), machine learning capabilities can be tested by comparing the ability of experimental computers to learn against that of human contestants. Recent tests indicate that humans and computers are about equal when learning image patterns are involved in specific tests. Machine learning has been advanced by designing new computers with processing functions that resemble that of the human brain (Markoff 2012).
Technology such as the computers designed by SAIL have proven that computers can exhibit some basic machine learning and problem-solving capabilities; however, such abilities have only been shown by computer technology in controlled situations such as tests. The random nature of Big Data may present serious challenges to advanced computers, even if a computer product has performed adequately in controlled test situations.
The task of analyzing Big Data may entail a high degree of random and unexpected variables in many instances, and such situations may confuse advanced neuronal inspired technology. Quantum computer technology may allow faster computer processing; however, whether or not quantum speed-up would give neuronal inspired computers AI capabilities is still unknown.
Neuronal inspired computer designs and quantum computing would most likely be of significant benefit to the field of predictive analytics. The exact synergy between neuronal inspired computing and quantum computing is yet to be seen. IBM’s Watson is one of the most prominent examples of possible AI-based predictive analytics products. There is the marketing initiative on the part of IBM to claim to the public that their corporation has invented computer technology which is capable of generating predictive analytics reports to customers without depending upon human beings beyond the products design stage and data collection. Whether or not this claim is true may be determined by an examination of literature that covers the creation and performance functionality of IBM’s Watson.
However, some critics may justifiably question whether or not winning a game of “Jeopardy!” actually necessitates problem-solving skills or not. There remains the possibility that Watson is nothing more than a computational prop designed to display to onlookers the appearance of AI. Quantum computer technology is not offered by the current version of Watson, but there is no reason found in the literature to believe that quantum computational functions would not be of great benefit to future versions of that analytical product offered by IBM.
The Turing Test is a popular method of ascertaining if computer technology has yet been invented that could be categorized as artificial intelligence. The Turing Test can be carried out at different times and places, and the execution of the testing methods may be in the hands of the event promoters and managers of such testing events.
According to a report from the University of Reading titled, Turing Test Success Marks Milestone in Computing History (2014), the Turing Test 2014, was an event in order to ascertain if artificial intelligence has yet been created by computer scientists in which computer programs are tested to be able to carry on conversations with people in order to be as convincing in conversation as a normal human being. The judges at the Turing Test 2014 were to determine if the conversationalists hosted at the event were humans or computers (Turing Test Success Marks Milestone in Computing History 2014).
The ability for a computer to carry out a lucid conversation in real-time with a human judge would require some fundamental elements of artificial intelligence; words that are used in language can oftentimes possess different meanings based on the nature of a conversation, and this reality can make the task of designing a conversationalist computer very complex.
The test designers may place some reasonable boundaries on what was allowed by the judges to be said in the conversations, or the tests could be limited to whatever the judge wants to ask the computer or person on the other side of the conversation; however, the Turing Test is meant to place practical standards of conversational abilities upon the computers that are entered in such events. Human beings may be more gullible than some others in the course of a conversation, and one judge at a Turing Test event may be much more observant and sceptical than a different judge that is also communicating with both humans and computers.
The use of multiple judges in Turing Test events makes good sense; according to Turing Test Success Marks Milestone in Computing History (2014), a Russian team designed a winning computer program which they named “Eugene” that mimicked a teenager when competing in the Turing Test 2014; the test’s criteria for passing depended on if any of the computer programs were able to trick the human judges thirty % of the time into believing the two-way conversation consisted of two human beings instead of a human and a computer program.
Teams that have created computer programs that competed in the Turing Test have not all been winners, but the Russian designed “Eugene” became a winner because the program was able to trick thirty-three percent of the judges that they were communicating with a human being instead of the computer program. The Eugene computer program may not have been able to perfectly mimic the ability of the standard human being in conversation, but the computer program was able to seem human enough to trick a sizable minority of human judges; the likelihood of more advanced computer programs being built upon the Eugene design is reasonably certain, and such new programs would probably possess performance improvements over Eugene.
The computer programs that compete in Turing Test events may be incrementally more and more advanced as the years go forward; this would make sense given the fact that computer technologies have been advancing over the past few decades. The advancing models of computers that are being submitted for competition in Turing Test events may give the event coordinators the chance to curtail the events in a way that allow for greater validity results when determining if a computer is capable of artificial intelligence.
According to Turing Test Success Marks Milestone in Computing History (2014), the Turing Test 2014 was conducted in a manner that was much more difficult for computer programs to pass; the conversations did not have restrictions imposed in terms of what could be asked to the programs, which raised the standards in terms of difficulty level for the computer scientists trying to create passable programs; such standards were in line with computer visionary Alan Turing’s original testing standards, and the program “Eugene” was the first computer product that ever passed a genuine Turing Test in recorded history, and is thus considered under the parameters of the Turing Test to be artificial intelligence (Turing Test Success Marks Milestone in Computing History 2014).
The Turing Test 2014 winner called Eugene and IBM’s Watson both appear to have been designed in a way that conforms and responds to human spoken language vernaculars; language acquisition may be a corner-stone of artificial intelligence attainment, which could explain the impressive performances of both Watson and the Eugene program. Quantum computing is still a very new field, and there does not exist a particularly large amount of functioning quantum computer products that exist during the present time.
D-Wave is possibly the most prominent example of a quantum computer product that currently exists in the world today; according to Metz (2015), the D-Wave is different from the typical theoretical designs of quantum computers. D-Wave is typically only useful in terms of solving optimization problems that involve equational tunnelling. D-Wave utilizes quantum annealing in order to solve problems. The computer design known as D-Wave is unique in the sense that the quantum bits actually run on super-conductor technology.
Constant energy flow in a circular pattern is used in D-Wave’s quantum bit design (Metz 2015). The D-Wave design is certainly innovative in terms of how the machine utilizes quantum particles in order to run qubits; however, D-Wave’s reliance upon quantum annealing may not have been the best approach for the machine’s designers to have taken. The fact that D-Wave is limited to the solving of optimization problems may be an indication that quantum annealing may not be the best method to design qubit based processors.
The D-Wave’s capacity to solve optimization problems may be of assistance to predictive analysts that process Big Data. The D-Wave’s specific optimization problem-solving capabilities may be of great use when Big Data needs to be crunched in order to produce predictive analytics reports; according to Brandom (2014), the D-Wave has been commercially advertised as a quantum computer that can crunch Big Data via a phenomenon known as quantum speed-up. The debate as to whether D-Wave is a true quantum computer has been put to rest; D-Wave is quantum because of the fact that quantum annealing is used during D-Wave’s processes. The debate as to whether or not D-Wave is faster than classical computer technology is still ongoing (Brandom 2014).
The D-Wave’s use of quantum annealing in the operation of the machine’s qubits shows that D-Wave is a true quantum computer. The D-Wave can only be truly valuable to Big Data analysts if that computer is truly able to exhibit quantum speed-up; otherwise, D-Wave may only be at the same usefulness to analysts as classical computer technology. The cost of developing, buying, or maintaining a quantum computer such as D-Wave is another factor that buyers would possibly want to consider before investing in such a quantum machine.
Marginal amounts of quantum speed-up over the performance of the best classical computer technology is exciting from a purely scientific point of view, but in order for a quantum computer to be further developed, the market for such a machine would logically also have to exist. The quantum speed-up on the D-Wave may have to be significantly noticeable in order for the developers of that computer to receive adequate funding needed for further development. One of the main problems that occur when computer scientists attempt to detect the phenomenon known as quantum speed-up is being able to understand what exactly took place during a quantum computer’s run time during performance tests.
Sometimes understanding what exact speed a quantum machine such as the D-Wave is running at during tests is complicated; according to Ronnow, Wang, Job, Boixo, Isakov, Wecker, Martinis, Lidar, and Troyer (2014), the D-Wave has been tested against classical computer technology in regard to processing speed. The researchers that conducted the tests between the D-Wave II and classical computer technology have speculated as to whether or not that version of D-Wave exhibited quantum speed-up during those particular tests.
The debate over D-Wave’s quantum speed-up or lack thereof is in regard to acute periods of computer performance, and not in concern to the general speed of D-Wave’s processing power in comparison to the classical computer technology that was pitted against that quantum machine (Ronnow, Wang, Job, Boixo, Isakov, Wecker, Martinis, Lidar, and Troyer 2014).
The best way to test D-Wave is to compare that machine to classical computer technology; however, the exact tests and methods of interpreting test results may be complicated. Validity concerns over whether or not quantum speed-up is being exhibited by D-Wave, and if the tests are measuring what needs to be measured may be issues of debate among researchers involved in D-Wave performance tests. The D-wave’s quantum speed-up capabilities are thus far shrouded in debate and confusion.
D-Wave has been produced in different versions, and the new version of D-Wave has also been tested in a performance test against classical computer technology, and the expectations that the new version of D-Wave can exhibit enhanced performance over the older versions have been high. The D-Wave has been invented to be able to calculate answers to specific types of problems, which involve equations involving tunnelling.
The D-Wave has been designed to run on special algorithms during run times, and those special algorithms are intended to allow for the machine to achieve quantum speed-up; according to Denchev, Sergio, Boixo, Isakov, Ding, Babbush, Amelyanskiy, Martinis, and Neven (2016), optimization problems that a quantum computer such as D-Wave may solve typically involve calculating how to best tunnel through walls of energy.
Classical computer technology can run algorithms in order to simulate calculations for certain types of tunnelling problems. The newer quantum machine model known as D-Wave Two-X has been compared in efficiency and speed tests against classical computer technology; the question that the researchers wanted to discover in the tests was if D-Wave’s quantum annealing processing methods are capable of exhibiting quantum speed-up and out-performing classical computer technology (Denchev, Sergio, Boixo, Isakov, Ding, Babbush, Amelyanskiy, Martinis, and Neven 2016). One prominent question among the computer science community is how much speed-up will be offered by the development and application of quantum computers in the effort at crunching data.
The method of quantum-based computation that takes advantage of the phenomenon known as quantum annealing may prove to be an efficient design for achieving quantum speed-up; otherwise, D-Wave may someday lose popularity as a quantum mechanical model. Brilliant individuals will most likely continue to work towards advancing quantum computing even if D-Wave is no longer being developed. The designers of the D-Wave are not the only experts that have been attempting to invent artificially intelligent computer technology.
The potential of the D-Wave can be discovered when different technology giants work together during the pioneering years of quantum computer development. Microsoft has been interested in the subject of artificial intelligence, and wants to know if the D-Wave would be of use in further developing AI technology; according to Brandom (2014), Google has bought a D-Wave quantum computer that uses quantum annealing during the system computational functions; Google worked with Microsoft to test the performance of their D-Wave computer against some of the most advanced non-quantum computers that currently exist on the market today.
Google is currently continuing its development of the D-Wave II system, which is hoped by the researchers working at Google’s Quantum AI Lab to exercise significant quantum speed-up when officially tested. The use of quantum annealing as a method is uncertain to yield notable quantum speed-ups (Brandom 2014). The fact that a pioneering quantum computer such as D-Wave, that uses the controversial method of quantum annealing as the primary method of computation shows that even a clumsy prototype of a quantum computer is as noteworthy as the very best traditional computer technology currently on the market.
The test run by Google and Microsoft concerning the D-Wave may suggest to optimists that advancements and improvements in the field of quantum computer technology would most likely lead to the phasing out of traditional computers on the market place. D-Wave’s use of quantum annealing may not be the best method in terms of quantum manipulation for computational purposes, and D-Wave is yet to be proven as inherently superior in comparison to traditional computer technology.
A quantum computer research and development team has achieved a limited but real example of machine learning through the use of quantum computational methods; according to First Demonstration of Artificial Intelligence on a Quantum Computer (2014), quantum computers can manipulate an atomic nucleus to solve calculations. Quantum bits can even solve different problems in parallel time.
Every time a qubit is added to the processing power of a computer, the power of that computer is made twice as strong (First Demonstration of Artificial Intelligence on a Quantum Computer 2014). Quantum speed-up may be an essential element that computers would need in order to exhibit AI functionality. A quantum computer has been tested in order to ascertain if that particular machine is able to perform machine learning.
The source by Li, Liu, Xu, and Du (2015), explains that computers using quantum parallelism based algorithms would theoretically be capable of functioning in the capacity of artificial intelligence. Quantum technology using four qubits has demonstrated the ability during pioneering testing, to learn how to recognize hand-written characters (Li, Liu, Xu, and Du 2014).
Therefore, the role of quantum computer technology may be an essential element needed in terms of designing computers that can produce predictive analytics which draws conclusions based on machine learning and problem-solving methods; machine learning is a mandatory characteristic that computers need in order to perform artificial intelligence-driven predictive analytics, and quantum computers may be better at machine learning than traditional computers.
The currently existing literature that has been reviewed by the research is abundant, but there nonetheless exist many gaps in knowledge at the present time. The major issue in question is if quantum computers will eventually be invented in a way that exponentially outperforms traditional computer technology. Quantum computer technology certainly appears to be possible, and already has been shown to exist in limited forms.
Classical computer technology is possibly able to perform some basic artificial intelligence level skills; however, the question of how much assistance quantum computer technology will be to the effort to invent higher level artificially intelligent computers is not fully understood by the literature at this time. The analysis undertaken in this research intends to further answer the question of how much further quantum computing will advance artificial intelligence that can be used in the field of predictive analytics.
The gaps in knowledge that happen to be present in the existing literature on the subject lead to the following research question: in what ways can artificial intelligence-based predictive analytics be advanced by quantum computing? A major question that the literature is able to address but fails to answer is if quantum computer technology can be invented in the near future that is inspired by a biological brain’s neuronal design.
Questions currently exist as to whether or not the human brain uses any form of quantum particle phenomenon in order to function; if the answer is found to be affirmative, the obvious option for computer scientists would be to model quantum computers after the brain’s quantum methods. The research intends to fill in the gaps in knowledge that exist in the literature; while completely finding the answers to such gaps in knowledge are not currently possible in the present time, the research does hope to help further answer the above-mentioned uncertainties.
